
import pandas as pd


path = 'data/viral_fp_curated.csv'
vfp_curated = pd.read_csv(path)


vfp_curated


dup = vfp_curated.duplicated(subset=['seq_vfp'], keep = False)
vfp_curated[dup]


counts = vfp_curated['seq_vfp'].value_counts()
counts

VYPFMWGGAYCFCDTENT                  12
VSLTLALLLGGLTMGGIAAGV               11
DRGWGNGCGLFGKG                      11
AVGIGALFLGFLGAAGSTMGA                9
GVFVLGFLGFLATAGSAMGAA                7
                                    ..
VVVSATLCSALYVGDLCGGVMLAAQMFIVSPQ     1
MVGAAALCSAMYLGDLCGGVFLVGQLFTFRPR     1
LAATSASLFPPWTAAAGVPFY                1
KASSRSAIEDLLFDKV                     1
DRGWGNGCGLFGKGSL                     1
Name: seq_vfp, Length: 216, dtype: int64


counts[counts>1]

VYPFMWGGAYCFCDTENT           12
VSLTLALLLGGLTMGGIAAGV        11
DRGWGNGCGLFGKG               11
AVGIGALFLGFLGAAGSTMGA         9
GVFVLGFLGFLATAGSAMGAA         7
                             ..
FIGAIIGGVALGVATAAQITAAAAL     2
VYPLLWGAAHCFCSTENT            2
WGCNPSDCPGVGTGCTACGLYLD       2
GVGLVIMLVIMAIVAAAGASL         2
LIGAIIGGVALGVATAAQITAASAL     2
Name: seq_vfp, Length: 70, dtype: int64


l_or = len(vfp_curated)
l_na_protein = len(vfp_curated.dropna(subset=['Sequence_fusogenic']))

print('The dataset origina has {} entries'.format(l_or))
print('This dataset contains {} entries of fusion peptides with fusion protein'.format(l_na_protein))
print('This dataset contains {} entries of fusion peptides without fusion protein'.format(l_or - l_na_protein))

vfp_uni = vfp_curated.drop_duplicates(subset=['seq_vfp'])
l_or_uni = len(vfp_uni)
l_na_protein_uni = len(vfp_uni.dropna(subset=['Sequence_fusogenic']))

print('######')
print('The dataset with just one entry per fusion peptide contains {} entries'.format(l_or_uni))
print('This dataset contains {} entries of fusion peptides with fusion protein'.format(l_na_protein_uni))
print('This dataset contains {} entries of fusion peptides without fusion protein'.format(l_or_uni - l_na_protein_uni))

The dataset origina has 403 entries
This dataset contains 379 entries of fusion peptides with fusion protein
This dataset contains 24 entries of fusion peptides without fusion protein
######
The dataset with just one entry per fusion peptide contains 216 entries
This dataset contains 192 entries of fusion peptides with fusion protein
This dataset contains 24 entries of fusion peptides without fusion protein


import matplotlib.pyplot as plt

fig, axs = plt.subplots(2,2, figsize=(20, 15))

plt.subplot(221)
fp_len = vfp_curated['seq_vfp'].str.len()
plt.hist(fp_len)
plt.title('len of fusion peptides w/ dup')

plt.subplot(222)
fp_len = vfp_curated['Sequence_fusogenic'].str.len()
plt.hist(fp_len)
plt.title('len of fusion proteins w/ dup')

plt.subplot(223)
fp_len = vfp_uni['seq_vfp'].str.len()
plt.hist(fp_len)
plt.title('len of fusion peptides w/o dup')

plt.subplot(224)
fp_len = vfp_uni['Sequence_fusogenic'].str.len()
plt.hist(fp_len)
plt.title('len of fusion proteins w/o dup')

plt.subplots_adjust( hspace=0.2, wspace=0.2)

plt.show()


vfp_curated[vfp_curated['seq_vfp'].str.len()>35]


vfp_curated[vfp_curated['seq_vfp'].str.len()>40]


counts = vfp_curated['Class'].value_counts()
print(counts)

counts_uni = vfp_uni['Class'].value_counts()
print(counts_uni)

fig, axs = plt.subplots(1,2, figsize=(20, 15))

plt.subplot(121)
plt.pie(counts, labels = counts.index)
plt.title('distribution of class w/ dup')

plt.subplot(122)
plt.pie(counts_uni, labels=counts.index)
plt.title('distribution of class w/o dup')
plt.legend()
plt.show()

I      261
II      78
III     27
Name: Class, dtype: int64
I      142
II      48
III      8
Name: Class, dtype: int64


counts = vfp_curated['Family'].value_counts()
print(counts)
print('#######\n \n \n')
counts_uni = vfp_uni['Family'].value_counts()
print(counts_uni)


fig, axs = plt.subplots(1,2, figsize=(20, 20))

plt.subplot(121)
plt.pie(counts, labels = counts.index)
plt.title('distribution of class w/ dup')

plt.subplot(122)
plt.pie(counts_uni, labels=counts.index) # , textprops={'fontsize': 10}
plt.title('distribution of class w/o dup')
# plt.legend(prop = {'size' : 10})
plt.subplots_adjust( hspace=0.2, wspace=0.5)

plt.show()

Retroviridae        124
Flaviviridae         69
Paramyxoviridae      36
Retroviridae         30
Togaviridae          27
Orthomyxoviridae     27
Herpesviridae        24
Filoviridae          15
Coronaviridae        14
Paramyxoviridae      13
Pneumoviridae         8
Arenaviridae          4
Peribunyaviridae      2
Bornaviridae          2
Bunyaviridae          2
Rhabdoviridae         2
Nairoviridae          1
Hepadnaviridae        1
Pneumoviridae         1
Phenuiviridae         1
Name: Family, dtype: int64
#######
 
 

Retroviridae        70
Flaviviridae        43
Paramyxoviridae     19
Retroviridae        13
Orthomyxoviridae    13
Coronaviridae       11
Paramyxoviridae      8
Togaviridae          8
Filoviridae          7
Pneumoviridae        5
Herpesviridae        5
Arenaviridae         4
Bornaviridae         2
Rhabdoviridae        2
Nairoviridae         1
Bunyaviridae         1
Hepadnaviridae       1
Peribunyaviridae     1
Pneumoviridae        1
Phenuiviridae        1
Name: Family, dtype: int64


different_fp = vfp_curated['seq_vfp'].unique()
print(len(different_fp))
different_fp = list(different_fp)


from Levenshtein import distance 
import copy 
def match(s1, s2):
    return distance(s1, s2) #<= 1


def get_dif(different_fp, dist_accepted):
    ls = []
    ls_peptides  = copy.deepcopy(different_fp)
    
    for peptide_1 in ls_peptides:
        count = -1 # to not cunt the peptide itself. 
#         ls_peptides.remove(peptide_1)
        for peptide_2 in ls_peptides:
            dist = match(peptide_1,peptide_2)
            if dist <dist_accepted+1: # aceita 2 mudancas - 3.  se aceita uma mudanca 2  
                count += 1
#                 ls_peptides.remove(peptide_2)
            else:
                pass
        ls.append(count)
    return ls 

ls_dif2 = get_dif(different_fp, dist_accepted = 2) # with 2 positions changed
ls_dif1 = get_dif(different_fp, dist_accepted = 1) # with just one position changed
ls_dif3 = get_dif(different_fp, dist_accepted = 3) # with 3 position changed


print('\n number of fpep with one difference in aminoacid referent to the fpep in that list positon. \n ')
print(sorted(ls_dif1, reverse=True))


print('\n number of fpep with2 difference in aminoacid referent to the fpep in that list positon. \n')
print(sorted(ls_dif2, reverse=True))


print('\n number of fpep with 3 difference in aminoacid referent to the fpep in that list positon. \n ')
print(sorted(ls_dif3, reverse=True))

216

 number of fpep with one difference in aminoacid referent to the fpep in that list positon. 
 
[5, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

 number of fpep with2 difference in aminoacid referent to the fpep in that list positon. 

[13, 12, 10, 9, 8, 7, 7, 7, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

 number of fpep with 3 difference in aminoacid referent to the fpep in that list positon. 
 
[21, 19, 18, 18, 17, 15, 15, 15, 15, 14, 13, 13, 11, 9, 9, 9, 9, 9, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]


# comapre theses sequences


#   cd-hit -i nr -o nr100 -c 1.00 -n 5 -M 16000 –d 0 -T 8
#   cd-hit -i db -o db90 -c 0.9 -n 5 -M 16000 –d 0 -T 8
#   where
#   db is the filename of input,
#   db90 is output, 
#   -c 1.0, means 100% identity, is the clustering threshold
#   -c 0.9, means 90% identity, is the clustering threshold
#   -n 5 is the word size
#   -d 0 use sequence name in fasta header till the first white space
#   -M 16000, to use 16GB RAM
#   -T 8, to use 8 threads
# Choose of word size:
# -n 5 for thresholds 0.7 ~ 1.0
# -n 4 for thresholds 0.6 ~ 0.7
# -n 3 for thresholds 0.5 ~ 0.6
# -n 2 for thresholds 0.4 ~ 0.5


# protein dataset in fasta format 
# output are two files: a fasta file of representative sequences and a text file of list of clusters.


print(len(vfp_curated))
# make fasta
from IO import make_fasta
make_fasta(df = vfp_curated , filename_fasta = 'fasta_curated.fasta' ,seq_column = 'seq_vfp')


# clustering 90 % similarity 
!cd-hit -i fasta_curated.fasta -o results_cdhit90 -c 0.9 -n 5 -M 16000 -d 0 -T 8
print('finished')


from IO import read_fasta
# retrieves a fasta file with representative sequences 
seq100 = read_fasta('fasta_curated.fasta', th = 100)
seq90 = read_fasta('results_cdhit90', th=90)

403
================================================================
Program: CD-HIT, V4.8.1 (+OpenMP), Apr 07 2021, 10:57:21
Command: cd-hit -i fasta_curated.fasta -o results_cdhit90 -c
         0.9 -n 5 -M 16000 -d 0 -T 8

Started: Sat Dec 24 11:21:27 2022
================================================================
                            Output                              
----------------------------------------------------------------
Warning: from file "fasta_curated.fasta",
Discarding invalid sequence or sequence without identifier and description!

>nan_G
[FRWYGPKY CGYATVT]
total seq: 402
longest and shortest : 47 and 13
Total letters: 9063
Sequences have been sorted

Approximated minimal memory consumption:
Sequence        : 0M
Buffer          : 8 X 10M = 84M
Table           : 2 X 65M = 130M
Miscellaneous   : 0M
Total           : 214M

Table limit with the given memory limit:
Max number of representatives: 4000000
Max number of word counting entries: 1973149542

# comparing sequences from          0  to        402
---------- new table with      120 representatives

      402  finished        120  clusters

Approximated maximum memory consumption: 214M
writing new database
writing clustering information
program completed !

Total CPU time 0.15
finished
number of sequences with 100 % threshold: 403
number of sequences with 90 % threshold: 120


# and a cluster file
seq90 = read_fasta('results_cdhit90.clstr', th=90)

# >Cluster 0
# 0	458aa, >P0DTC2_Spike... *
# >Cluster 1
# 0	91aa, >D3IZQ2_Viral... *
# >Cluster 2

number of sequences with 90 % threshold: 120


# clustering 80 % similarity 
!cd-hit -i fasta_curated.fasta -o results_cdhit80 -c 0.8 -n 5 -M 16000 -d 0 -T 8
print('finished')

# clustering 50 % similarity 
!cd-hit -i fasta_curated.fasta -o results_cdhit50 -c 0.5 -n 3 -M 16000 -d 0 -T 8 # for thresholds lower word size must be increased
print('finished')

from IO import read_fasta
# retrieves a fasta file with representative sequences 
seq100 = read_fasta('fasta_curated.fasta', th = 100)
seq80 = read_fasta('results_cdhit80', th=80)
seq50 = read_fasta('results_cdhit50', th=50)

================================================================
Program: CD-HIT, V4.8.1 (+OpenMP), Apr 07 2021, 10:57:21
Command: cd-hit -i fasta_curated.fasta -o results_cdhit80 -c
         0.8 -n 5 -M 16000 -d 0 -T 8

Started: Sat Dec 24 11:21:27 2022
================================================================
                            Output                              
----------------------------------------------------------------
Warning: from file "fasta_curated.fasta",
Discarding invalid sequence or sequence without identifier and description!

>nan_G
[FRWYGPKY CGYATVT]
total seq: 402
longest and shortest : 47 and 13
Total letters: 9063
Sequences have been sorted

Approximated minimal memory consumption:
Sequence        : 0M
Buffer          : 8 X 10M = 84M
Table           : 2 X 65M = 130M
Miscellaneous   : 0M
Total           : 214M

Table limit with the given memory limit:
Max number of representatives: 4000000
Max number of word counting entries: 1973149542

# comparing sequences from          0  to        402
---------- new table with       91 representatives

      402  finished         91  clusters

Approximated maximum memory consumption: 214M
writing new database
writing clustering information
program completed !

Total CPU time 0.16
finished
================================================================
Program: CD-HIT, V4.8.1 (+OpenMP), Apr 07 2021, 10:57:21
Command: cd-hit -i fasta_curated.fasta -o results_cdhit50 -c
         0.5 -n 3 -M 16000 -d 0 -T 8

Started: Sat Dec 24 11:21:28 2022
================================================================
                            Output                              
----------------------------------------------------------------
Warning: from file "fasta_curated.fasta",
Discarding invalid sequence or sequence without identifier and description!

>nan_G
[FRWYGPKY CGYATVT]
total seq: 402
longest and shortest : 47 and 13
Total letters: 9063
Sequences have been sorted

Approximated minimal memory consumption:
Sequence        : 0M
Buffer          : 8 X 10M = 84M
Table           : 2 X 0M = 0M
Miscellaneous   : 0M
Total           : 84M

Table limit with the given memory limit:
Max number of representatives: 4000000
Max number of word counting entries: 1989448902

# comparing sequences from          0  to        402
---------- new table with       40 representatives

      402  finished         40  clusters

Approximated maximum memory consumption: 84M
writing new database
writing clustering information
program completed !

Total CPU time 0.08
finished
number of sequences with 100 % threshold: 403
number of sequences with 80 % threshold: 91
number of sequences with 50 % threshold: 40


from Bio import motifs
import logomaker
def create_log(list_seqs):
    maxlen = len(max(list_seqs, key = len)) 
    seq_pad = [str(seq).ljust(maxlen, 'X') for seq in list_seqs if '['not in str(seq)]
    
    m = motifs.create(seq_pad, alphabet= 'ACDEFGHIKLMNPQRSTVWYX')

    # create Logo object
    ww_logo = logomaker.Logo(pd.DataFrame(m.counts),
                             color_scheme='NajafabadiEtAl2017',
                             vpad=.1,
                             width=.8)

    print(m.consensus)
          

          
create_log(list_seqs = seq100)
# create_log(list_seqs = seq90)
create_log(list_seqs = seq80)
create_log(list_seqs = seq50)

 Warning: Character 'X' is not in color_dict. Using black.
GVGAAAGLGALGGAAAATXGAXXXXXXXXXXXXXXXXXXXXXXXXXX
 Warning: Character 'X' is not in color_dict. Using black.
GVGLAIALGALGVGAAAGAGXXXXXXXXXXXXXXXXXXXXXXXXXXX
 Warning: Character 'X' is not in color_dict. Using black.
GLGLLLLLGGALAGAAASLAXXXXXXXXXXXXXXXXXXXXXXXXXXX


for family in vfp_curated['Family'].unique():
    vfp_fam = vfp_uni.loc[vfp_uni['Family']==family] # using not the duplicates
    list_seqs = vfp_fam['seq_vfp']
    print(family)
    create_log(list_seqs)

Retroviridae
 Warning: Character 'X' is not in color_dict. Using black.
AVGLLGFLGGLLGAAGGTMGAXXXXXXXXXXXXXXXXXX
Pneumoviridae
 Warning: Character 'X' is not in color_dict. Using black.
FVEGAIALGVATAAAVTAGVAIXXXXXXX
Retroviridae 
 Warning: Character 'X' is not in color_dict. Using black.
VMLALATLLSGAAAGTGATAIXX
Coronaviridae
 Warning: Character 'X' is not in color_dict. Using black.
SAATGAIADFGWFAFAXSXXXXXXX
Bornaviridae 
 Warning: Character 'X' is not in color_dict. Using black.
ILQSLLLGVFGTGIASASQFLRGWLNHPDIIGYIVNGVGVVW
Paramyxoviridae
 Warning: Character 'X' is not in color_dict. Using black.
FAGVVIGGAALGVATAAQITAGVALXXXXXX
Peribunyaviridae
 Warning: Character 'X' is not in color_dict. Using black.
WGCEEFGCLAVSDGCVFGSCQD
Togaviridae
 Warning: Character 'X' is not in color_dict. Using black.
VYPFMWGGAYCFCDSENTXXX
Nairoviridae
 Warning: Character 'X' is not in color_dict. Using black.
WRCNPTWCWGVGTGCTCCGLDVK
Flaviviridae
 Warning: Character 'X' is not in color_dict. Using black.
LVGAATLCSALYVGDLCGSVFLVGQLFTFSPR
Filoviridae
 Warning: Character 'X' is not in color_dict. Using black.
GAAAGLAWIPYFGPAAXXXXX
Herpesviridae
 Warning: Character 'X' is not in color_dict. Using black.
PDFFRLAFLLAVSGFAFVNAA
Bunyaviridae
 Warning: Character 'X' is not in color_dict. Using black.
WGCNPSDCPGVGTGCTACGLYLD
Hepadnaviridae
 Warning: Character 'X' is not in color_dict. Using black.
MENITSGFLGPLLVLQAGFFLLTR
Orthomyxoviridae
 Warning: Character 'X' is not in color_dict. Using black.
GLFGAIAGFIENGWEGMIDGWYGFRXXXXXXXXXXXXX
Arenaviridae
 Warning: Character 'X' is not in color_dict. Using black.
GTFTWTLSDSEGKDTPGGYCLTRWMLIAAELKCFGNTAV
Pneumoviridae 
 Warning: Character 'X' is not in color_dict. Using black.
FLGLILGLGAAVTAGVALAKT
Paramyxoviridae 
 Warning: Character 'X' is not in color_dict. Using black.
FIGAIIGGVALGVATAAQITAASAL
Rhabdoviridae
 Warning: Character 'X' is not in color_dict. Using black.
NPYPDYHWLRTVKTTKESLVIISPSVADLDPYDRSLHSRVFPGGNCS
Phenuiviridae
 Warning: Character 'X' is not in color_dict. Using black.
WGCGCFNVNPSCLFVHT


cluster90 = 'results_cdhit50.clstr'
fasta_or = 'fasta_curated.fasta'

# >Cluster 0
# 0	47aa, >P08667_Glycoprotein... *
# >Cluster 1
# 0	42aa, >P52638_Envelope... *
# 1	42aa, >Q8BB27_Envelope... at 97.62%
# >Cluster 2
# 0	39aa, >P26313_Pre-glycoprotein... *
# 1	39aa, >nan_glycoprotein... at 94.87%

# >P03386_Envelope glycoprotein_I_0
# VSLTLALLLGGLTMGGIAAGV
# >P03397_Envelope glycoprotein gp95_I_1
# GPTARIFASILAPGVAAAQAL

# transform fasta_or in dataframe 
from Bio import SeqIO

dict_or = dict()
with open(fasta_or) as fasta_file:
    for seq_record in SeqIO.parse(fasta_file, 'fasta'):  # (generator)
        dict_or[seq_record.id] = seq_record.seq


import re
def read_cluster_file(cl_file, dict_or):
    dict_cluster = dict()
    key_list = []
    cluster_name = 'None'
    with open(cl_file) as cluster_file: 
        for line in cluster_file:
           
            if line.startswith('>'):
                dict_cluster[cluster_name] = key_list # from previous 
                # reset the name and the list 
                cluster_name = line # Cluster 0
                key_list = []
            
            else:
                m = re.search('>(.+?)\\.', line)
                name = m.group(1)
#                 name = 'between Z and ...' #>nan_HA_I_247...
                key_list.append(dict_or[name])
    dict_cluster.pop('None')
    return dict_cluster
clusters = read_cluster_file(cl_file=cluster90, dict_or=dict_or)
for list_seqs in clusters.values():
    create_log(list_seqs)

 Warning: Character 'X' is not in color_dict. Using black.
NPYPDYHWLRTVKTTKESLVIISPSVADLDPYDRSLHSRVFPGGNCS
 Warning: Character 'X' is not in color_dict. Using black.
ILQSLLLGVFGTGIASASQFLRGWLNHPDIIGYIVNGVGVVW
 Warning: Character 'X' is not in color_dict. Using black.
GTFTWTLSDSSGKDTPGGYCLTEWMLIAAELKCFGNTAV
 Warning: Character 'X' is not in color_dict. Using black.
GLFGAIAGFIEGGWEGMIDGWYGFRXXXXXXXXXXXXX
 Warning: Character 'X' is not in color_dict. Using black.
LVGAATLCSALYVGDLCGSVFLVGQLFTFSPR
 Warning: Character 'X' is not in color_dict. Using black.
FAGVVIGGAALGVATAAQITAAIALXXXXXX
 Warning: Character 'X' is not in color_dict. Using black.
NEESYYHIAARIATSIFALSEMGRTTEYF
 Warning: Character 'X' is not in color_dict. Using black.
SSLLGSIAGAGWTAGLSSFAAIPFA
 Warning: Character 'X' is not in color_dict. Using black.
MENITSGFLGPLLVLQAGFFLLTR
 Warning: Character 'X' is not in color_dict. Using black.
WGCNPSDCPGVGTGCTACGLYLD
 Warning: Character 'X' is not in color_dict. Using black.
NLRRIQEAGLGLANAITTVAKIS
 Warning: Character 'X' is not in color_dict. Using black.
NYAKLRSMGYALTGAVQTLSQIS
 Warning: Character 'X' is not in color_dict. Using black.
WGCEEFGCLAVSDGCVFGSCQD
 Warning: Character 'X' is not in color_dict. Using black.
AVGVGGFFLGFLGAAGSTMGAX
 Warning: Character 'X' is not in color_dict. Using black.
VSLTLALLLGGLTMGGIAAGV
 Warning: Character 'X' is not in color_dict. Using black.
GPTARIFASILAPGVAAAQAL
 Warning: Character 'X' is not in color_dict. Using black.
FIPLLVGLGITTAVSTGTTGL
 Warning: Character 'X' is not in color_dict. Using black.
AVGLAIFLLVLAIMAITASLT
 Warning: Character 'X' is not in color_dict. Using black.
FLGFLLGVGSAIASGVAVSKV
 Warning: Character 'X' is not in color_dict. Using black.
GIGLVIVLAIMAIIAAAGAGL
 Warning: Character 'X' is not in color_dict. Using black.

/home/martinha/miniconda3/envs/viral_fp/lib/python3.9/site-packages/logomaker/src/Logo.py:194: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  fig, ax = plt.subplots(1, 1, figsize=self.figsize)

TETLTTMFEVSVAFFKVGHAV
 Warning: Character 'X' is not in color_dict. Using black.
FGISAIVAAIVAATAIAASAT
 Warning: Character 'X' is not in color_dict. Using black.
VMLALATVLSMAGAGTGATAI
 Warning: Character 'X' is not in color_dict. Using black.
LAATSASLFPPWTAAAGVPFY
 Warning: Character 'X' is not in color_dict. Using black.
IGGIALGGLTSAVSIPFSLAI
 Warning: Character 'X' is not in color_dict. Using black.
PLFWRLTGLLATSGFAFVNAA
 Warning: Character 'X' is not in color_dict. Using black.
KDTFHLMFLFGLTHFLYSTRG
 Warning: Character 'X' is not in color_dict. Using black.
AVPVAVWLVSALAMGAGVAGG
 Warning: Character 'X' is not in color_dict. Using black.
GAAWGLAWIPYFGPAAXXXXX
 Warning: Character 'X' is not in color_dict. Using black.
FVAAIILGISALIAIITSFAV
 Warning: Character 'X' is not in color_dict. Using black.
VYPFMWGGAYCFCDTENTXXX
 Warning: Character 'X' is not in color_dict. Using black.
RRTVEMAFAYALALFAAARQ
 Warning: Character 'X' is not in color_dict. Using black.
PDIFLVLFQMLVAHFLVARG
 Warning: Character 'X' is not in color_dict. Using black.
MYKTPAIKDFGGFNFSQIL
 Warning: Character 'X' is not in color_dict. Using black.
GIPQMMFLYGIVHFSYSTK
 Warning: Character 'X' is not in color_dict. Using black.
GTEVSEALGGAGLTGGFY
 Warning: Character 'X' is not in color_dict. Using black.
RSARSAIEDLLFDKVXXX
 Warning: Character 'X' is not in color_dict. Using black.
WGCGCFNVNPSCLFVHT
 Warning: Character 'X' is not in color_dict. Using black.
DRGWGNGCGLFGKGXX

	idProtein	Name	Class	Activation	Name_Fusogenic_Unit	Location_Fusogenic	Sequence_fusogenic	UniProtID	NcbiID	idTaxonomy	...	idTaxonomy.1	CommonName	Family	Genre	Species	SubSpecies	NcbiTax	FP corrected DL	Notes DL	seq_vfp
0	2	Envelope glycoprotein	I	binding to receptor	Envelope protein p15E	471-650	EPVSLTLALLLGGLTMGGIAAGVGTGTTALVATQQFQQLQAAMHDD...	P03386	NaN	2	...	2	AKV murine leukemia virus (AKR (endogenous) mu...	Retroviridae	Gammaretrovirus	AKR murine leukemia virus	NaN	11791	NaN	NaN	VSLTLALLLGGLTMGGIAAGV
1	4	Envelope glycoprotein gp95	I	binding to receptor	gp37	402-606	STSHLDDTCSDEVQLWGPTARIFASILAPGVAAAQALREIERLACW...	P03397	NaN	4	...	4	Avian leukosis virus RSA (RSV-SRA) (Rous sarco...	Retroviridae	Alpharetrovirus	Rous sarcoma virus	strain Schmidt-Ruppin A	269446	NaN	NaN	GPTARIFASILAPGVAAAQAL
2	5	Fusion glycoprotein F0	I	binding to receptor	F1	103-537	FVLGAIALGVATAAAVTAGVAIAKTIRLEGEVAAIKGALRKTNEAV...	Q2Y2M3	NaN	5	...	5	Avian metapneumovirus (isolate Canada goose/Mi...	Pneumoviridae	Metapneumovirus	Avian metapneumovirus	15a	652954	NaN	NaN	FVLGAIALGVATAAAVTAGVAI
3	6	Envelope glycoprotein	I	binding to receptor	gp22	393-582	AVQFIPLLVGLGITGATLAGGTGLGVSVHTYHKLSNQLIEDVQALS...	P03399	NaN	6	...	6	Avian reticuloendotheliosis virus	Retroviridae	Gammaretrovirus	Reticuloendotheliosis virus	NaN	11636	NaN	NaN	FIPLLVGLGITGATLAGGTGL
4	7	Envelope glycoprotein	I	binding to receptor	gp37	79-257	SVSHLDDTCSDEVQLWGPTARIFASILAPGVAAAQALREIERLACW...	P33498	NaN	7	...	7	Avian retrovirus RPL30	Retroviridae	Alpharetrovirus	Avian leukosis virus	RPL30	31671	NaN	NaN	GPTARIFASILAPGVAAAQAL
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
398	798	Genome polyprotein	NaN	binding to receptor	Envelop protein E	286 - 778	AHCIGVADRDFIEGVHGGTWVSATLEQDKCVTVMAPDKPSLDISLE...	Q9YRV3	NaN	798	...	798	Yellow fever virus	Flaviviridae	Flavivirus	Yellow fever virus	Trinidad/79A/1979	407137	NaN	NaN	DRGWGNGCGLFGKG
399	799	Genome polyprotein	NaN	binding to receptor	Envelop protein E	286 - 778	AHCIGITDRDFIEGVHGGTWVSASLEQDKCVTVMAPDKPSLDISLQ...	Q1X880	NaN	799	...	799	Yellow fever virus	Flaviviridae	Flavivirus	Yellow fever virus	isolate Uganda/A7094A4/1948	407139	NaN	NaN	DRGWGNGCGLFGKG
400	800	Genome polyprotein	NaN	binding to receptor	Envelop protein E	286 - 778	AHCIGITDRDFIEGVHGGTWVSATLEQGKCVTVMAPDKPSLDISLQ...	Q074N0	NaN	800	...	800	Yellow fever virus	Flaviviridae	Flavivirus	Yellow fever virus	isolate Ethiopia/Couma/1961	407141	NaN	NaN	DRGWGNGCGLFGKG
401	801	Genome polyprotein	NaN	binding to receptor	Envelop protein E	268 - 778	AHCIGITDRDFIEGVHGGTWVSATLEQDKCVTVMAPDKPSLDISLE...	Q89277	NaN	801	...	801	Yellow fever virus	Flaviviridae	Flavivirus	Yellow fever virus	strain French neurotropic vaccine	407135	NaN	NaN	DRGWGNGCGLFGKG
402	802	Genome polyprotein	NaN	binding to receptor	Envelop protein E	291 - 790	IRCIGVSNRDFVEGMSGGTWVDVVLEHGGCVTVMAQDKPTVDIELV...	Q32ZE1	NaN	802	...	802	Zika virus	Flaviviridae	Flavivirus	Zika virus	NaN	64320	NaN	NaN	DRGWGNGCGLFGKGSL

	idProtein	Name	Class	Activation	Name_Fusogenic_Unit	Location_Fusogenic	Sequence_fusogenic	UniProtID	NcbiID	idTaxonomy	...	idTaxonomy.1	CommonName	Family	Genre	Species	SubSpecies	NcbiTax	FP corrected DL	Notes DL	seq_vfp
0	2	Envelope glycoprotein	I	binding to receptor	Envelope protein p15E	471-650	EPVSLTLALLLGGLTMGGIAAGVGTGTTALVATQQFQQLQAAMHDD...	P03386	NaN	2	...	2	AKV murine leukemia virus (AKR (endogenous) mu...	Retroviridae	Gammaretrovirus	AKR murine leukemia virus	NaN	11791	NaN	NaN	VSLTLALLLGGLTMGGIAAGV
1	4	Envelope glycoprotein gp95	I	binding to receptor	gp37	402-606	STSHLDDTCSDEVQLWGPTARIFASILAPGVAAAQALREIERLACW...	P03397	NaN	4	...	4	Avian leukosis virus RSA (RSV-SRA) (Rous sarco...	Retroviridae	Alpharetrovirus	Rous sarcoma virus	strain Schmidt-Ruppin A	269446	NaN	NaN	GPTARIFASILAPGVAAAQAL
2	5	Fusion glycoprotein F0	I	binding to receptor	F1	103-537	FVLGAIALGVATAAAVTAGVAIAKTIRLEGEVAAIKGALRKTNEAV...	Q2Y2M3	NaN	5	...	5	Avian metapneumovirus (isolate Canada goose/Mi...	Pneumoviridae	Metapneumovirus	Avian metapneumovirus	15a	652954	NaN	NaN	FVLGAIALGVATAAAVTAGVAI
4	7	Envelope glycoprotein	I	binding to receptor	gp37	79-257	SVSHLDDTCSDEVQLWGPTARIFASILAPGVAAAQALREIERLACW...	P33498	NaN	7	...	7	Avian retrovirus RPL30	Retroviridae	Alpharetrovirus	Avian leukosis virus	RPL30	31671	NaN	NaN	GPTARIFASILAPGVAAAQAL
7	11	Spike glycoprotein	I	binding to receptors	S2	750-1235	SYSASQFQLAVLNYTSPIVVTPINSSGFTAAIPTNFSFSLTQEYIE...	Q0Q4F2	NaN	11	...	11	Bat coronavirus 133/2005 (BtCoV) (BtCoV/133/2005)	Coronaviridae	Betacoronavirus	Tylonycteris bat coronavirus HKU4	BtCoV/133/2005	389230	NaN	NaN	SSLLGSIAGAGWTAGLSSFAAIPFA
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
397	797	Genome polyprotein	NaN	binding to receptor	Envelop protein E	286 - 778	AHCIGITDRDFIEGVHGGTWVSATLEQDKCVTVMAPDKPSLDISLQ...	Q1X881	NaN	797	...	797	Yellow fever virus	Flaviviridae	Flavivirus	Yellow fever virus	isolate Angola/14FA/1971	407140	NaN	NaN	DRGWGNGCGLFGKG
398	798	Genome polyprotein	NaN	binding to receptor	Envelop protein E	286 - 778	AHCIGVADRDFIEGVHGGTWVSATLEQDKCVTVMAPDKPSLDISLE...	Q9YRV3	NaN	798	...	798	Yellow fever virus	Flaviviridae	Flavivirus	Yellow fever virus	Trinidad/79A/1979	407137	NaN	NaN	DRGWGNGCGLFGKG
399	799	Genome polyprotein	NaN	binding to receptor	Envelop protein E	286 - 778	AHCIGITDRDFIEGVHGGTWVSASLEQDKCVTVMAPDKPSLDISLQ...	Q1X880	NaN	799	...	799	Yellow fever virus	Flaviviridae	Flavivirus	Yellow fever virus	isolate Uganda/A7094A4/1948	407139	NaN	NaN	DRGWGNGCGLFGKG
400	800	Genome polyprotein	NaN	binding to receptor	Envelop protein E	286 - 778	AHCIGITDRDFIEGVHGGTWVSATLEQGKCVTVMAPDKPSLDISLQ...	Q074N0	NaN	800	...	800	Yellow fever virus	Flaviviridae	Flavivirus	Yellow fever virus	isolate Ethiopia/Couma/1961	407141	NaN	NaN	DRGWGNGCGLFGKG
401	801	Genome polyprotein	NaN	binding to receptor	Envelop protein E	268 - 778	AHCIGITDRDFIEGVHGGTWVSATLEQDKCVTVMAPDKPSLDISLE...	Q89277	NaN	801	...	801	Yellow fever virus	Flaviviridae	Flavivirus	Yellow fever virus	strain French neurotropic vaccine	407135	NaN	NaN	DRGWGNGCGLFGKG

	idProtein	Name	Class	Activation	Name_Fusogenic_Unit	Location_Fusogenic	Sequence_fusogenic	UniProtID	NcbiID	idTaxonomy	...	idTaxonomy.1	CommonName	Family	Genre	Species	SubSpecies	NcbiTax	FP corrected DL	Notes DL	seq_vfp
13	17	Envelope glycoprotein p57	II	ph	Envelope glycoprotein p29	250-503	DTQQIEYLVHKLRPTLKDAWEDCEILQSLLLGVFGTGIASASQFLR...	P52638	NaN	17	...	17	Borna disease virus (strain V) (BDV)	Bornaviridae	NaN	Borna disease virus	strain V	928296	NaN	NaN	ILQSLLLGVFGTGIASASQFLRSWLNHPDIIGYIVNGVGVVW
14	18	Envelope glycoprotein p57	II	ph	Envelope glycoprotein p29	250-503	DTQQIEYLVHKLRPTLKDAWEDCEILQSLLLGVFGTGIASASQFLR...	Q8BB27	NaN	18	...	18	Borna disease virus 1 (BoDV-1)	Bornaviridae	Orthobornavirus	Mammalian 1 orthobornavirus	1	1714621	NaN	NaN	ILQSLLLGVFGTGIASASQFLRGWLNHPDIIGYIVNGVGVVW
253	334	HA	I	NaN	HA2 subunit	NaN	NaN	NaN	NaN	316	...	316	Infectious salmon anemia virus (isolate Atlant...	Orthomyxoviridae	Isavirus	Salmon isavirus	isolate 810/9/99	652965	NaN	NaN	GIFGAIAGFIENGWEGMVDGWYGFRHQNSEGTGQAADL
270	559	Pre-glycoprotein polyprotein GP complex	I	low pH	Glycoprotein G2	252-485	AFFSWSLTDSSGKDTPGGYCLEEWMLVAAKMKCFGNTAVAKCNLNH...	P26313	NaN	558	...	558	Jembrana disease virus (JDV)	Retroviridae	Lentivirus	Jembrana disease virus	NaN	36370	NaN	NaN	AFFSWSLTDSSGKDTPGGYCLEEWMLVAAKMKCFGNTAV
280	570	Pre-glycoprotein polyprotein GP complex	I	low pH	GP2	260-491	GTFTWTLSDSEGKDTPGGYCLTRWMLIEAELKCFGNTAVAKCNEKH...	P08669	NaN	570	...	570	Lassa virus	Arenaviridae	Mammarenavirus	Lassa mammarenavirus	strain Josiah	11622	GTFTWTLSDSEGKDTPGGYCLTRWMLIEAELKCFGNTAV	According to https://doi.org/10.1099/vir.0.829...	GTFTWTLSDSEGKDTPGGYCLTRWMLIEAELKCFGNTAV
281	571	Pre-glycoprotein polyprotein GP complex	I	low pH	GP2	259-490	GTFTWTLSDSEGNETPGGYCLTRWMLIEAELKCFGNTAVAKCNEKH...	P17332	NaN	571	...	571	Lassa virus	Arenaviridae	Mammarenavirus	Lassa mammarenavirus	GA391	11621	GTFTWTLSDSEGNETPGGYCLTRWMLIEAELKCFGNTAV	Same as prevois, but from a different strain	GTFTWTLSDSEGNETPGGYCLTRWMLIEAELKCFGNTAV
283	574	Pre-glycoprotein polyprotein GP complex	I	low pH	GP2	266-498	GTFTWTLSDSSGVENPGGYCLTKWMILAAELKCFGNTAVAKCNVNH...	P07399	NaN	574	...	574	Lymphocytic choriomeningitis virus	Arenaviridae	Mammarenavirus	Lymphocytic choriomeningitis mammarenavirus	(strain WE)	11627	GTFTWTLSDSSGVENPGGYCLTKWMILAAELKCFGNTAV	Same as Lassa	GTFTWTLSDSSGVENPGGYCLTKWMILAAELKCFGNTAV
284	576	glycoprotein precursor	I	low pH	GP2	496	MGQLISFFQEIPVFLQEALNIALVAVSLIAVIKGIINLYKSGLFQF...	NaN	AAS77879.1	576	...	576	Machupo virus	Arenaviridae	Mammarenavirus	Machupo mammarenavirus	NaN	11628	AFFSWSLTDSSGKDMPGGYCLEEWMLIAAKMKCFGNTAV	Same as Lassa	AFFSWSLTDSSGKDMPGGYCLEEWMLIAAKMKCFGNTAV
330	659	Glycoprotein G	NaN	low pH	NaN	20-524	KFPIYTIPDKLGPWSPIDIHHLSCPNNLVVEDEGCTNLSGFSYMEL...	P08667	NaN	659	...	659	Rabies virus	Rhabdoviridae	Lyssavirus	Rabies lyssavirus	strain Pasteur vaccin	103929	NaN	NaN	NPYPDYHWLRTVKTTKESLVIISPSVADLDPYDRSLHSRVFPGGNCS

	idProtein	Name	Class	Activation	Name_Fusogenic_Unit	Location_Fusogenic	Sequence_fusogenic	UniProtID	NcbiID	idTaxonomy	...	idTaxonomy.1	CommonName	Family	Genre	Species	SubSpecies	NcbiTax	FP corrected DL	Notes DL	seq_vfp
13	17	Envelope glycoprotein p57	II	ph	Envelope glycoprotein p29	250-503	DTQQIEYLVHKLRPTLKDAWEDCEILQSLLLGVFGTGIASASQFLR...	P52638	NaN	17	...	17	Borna disease virus (strain V) (BDV)	Bornaviridae	NaN	Borna disease virus	strain V	928296	NaN	NaN	ILQSLLLGVFGTGIASASQFLRSWLNHPDIIGYIVNGVGVVW
14	18	Envelope glycoprotein p57	II	ph	Envelope glycoprotein p29	250-503	DTQQIEYLVHKLRPTLKDAWEDCEILQSLLLGVFGTGIASASQFLR...	Q8BB27	NaN	18	...	18	Borna disease virus 1 (BoDV-1)	Bornaviridae	Orthobornavirus	Mammalian 1 orthobornavirus	1	1714621	NaN	NaN	ILQSLLLGVFGTGIASASQFLRGWLNHPDIIGYIVNGVGVVW
330	659	Glycoprotein G	NaN	low pH	NaN	20-524	KFPIYTIPDKLGPWSPIDIHHLSCPNNLVVEDEGCTNLSGFSYMEL...	P08667	NaN	659	...	659	Rabies virus	Rhabdoviridae	Lyssavirus	Rabies lyssavirus	strain Pasteur vaccin	103929	NaN	NaN	NPYPDYHWLRTVKTTKESLVIISPSVADLDPYDRSLHSRVFPGGNCS

Repeats on fusion peptides¶

Dataset entries without fusion protein¶

sizes of fusion proteins and fusion peptide¶

Distribution across fusion peptide classes and family virus¶

seqs that only differ in one, 2 or 3 character¶

similarity CDHIT¶

WEBLOGOS¶